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Coevolution with Bee Colony: Paradigm Shift in Many Objective Optimization

Coevolution with Bee Colony: Paradigm Shift in Many Objectiv...

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khoirom, Selina Pramanik, Shreya Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The research offers a unique coevolution-based many-objective optimization (MaOO) approach to benefit from the underlying parallelism of the evolutionary process. The proposed MaOO handles individual objectives in parallel using artificial bee colony (ABC) algorithm. After achieving convergence, a group of good-quality solutions is carefully chosen from each ABC population, dealing with a specific objective. Next, the groups are combined to form a union set. Finally, a fuzzy membership-induced rank measure is developed to discover the top-ranked equally good members of the union set, showing the approximate Pareto optimal solutions to the given MaOO problem. The proposed MaOO algorithm, referred to as fuzzy-bee colony (FBC) is compared with three state-of-the-art techniques. Experiments undertaken reveal that FBC outperforms its contenders with respect to the performance metrics. © 2024 IEEE.

关键词： Optimization algorithms

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Automobile Entry System Based on Face Recognition

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 573-585页

作者： Fang, Weidong Yu, Zuoxi Pan, Jeng-Shyang School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Chaoyang University of Technology Taichung413310 Taiwan

With the development of technology, the automobile has become an indispensable part of people’s daily lives. People’s needs for automobile entry systems have also changed, in automobile safety and ease of use have become more and more important. In recent years, face recognition technology has made significant progress, and face recognition technology has been widely used in various fields, especially face recognition based on deep learning has great advantages in accuracy, recognition speed, and security, and can provide a more secure and reliable way of identity verification. Traditional automobile entry systems usually use mechanical keys and remote control keys, which do not remove the key and have certain shortcomings in security and user experience. Face recognition-based car entry systems can make up for these shortcomings and provide a more convenient and intuitive user experience. Combining face recognition with automobiles is also a hot topic in current scientific research. In this paper, a set of automobile entry systems with high efficiency and security is designed according to the face recognition method research, using three deep learning models: face detection, live body detection, and face recognition. In face detection, the RetinaFace lightweight model is used the network structure is improved, and the detection speed is increased by 14.9%. For face live detection and face recognition, the MobileFaceNet lightweight network is used as the base network for live detection and face recognition, achieving a 98.9% accuracy rate on the CelebA Spoof live detection dataset. In face recognition, feature extraction is performed on the detected faces after face detection, and the recognition results are output by comparing with the recorded faces. Improvements to its network improved the recognition accuracy by 0.18%, 0.77%, and 0.73% on the LFW, CFP FP, and AgeDB30 datasets, respectively. The model was deployed on Raspberry Pi and connected to CANoe via CAN bu

关键词： Face recognition

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Markov Model Based Risk Prediction of Patients with Kidney Diseases

Markov Model Based Risk Prediction of Patients with Kidney D...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chaki, Shambhab Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The paper proposes a novel machine learning approach for early prediction of risk of a patient suffering from severe kidney-related diseases (KD). The training phase consists of two steps. First, the records of the already existing patients with KD are divided into groups based on the similarity of the sequences of their clinical activities, including diagnoses, lab tests and medications. Next, a Markov chain model is employed to mathematically represent the clinical activity-sequences of patients in each group. The prediction phase also consists of two steps. First, the support of each group to the clinical-activities of the unknown patient P is evaluated. P is then assigned with the group with the highest support. Next, the Markov chain model of the winning group is used to predict the possible finite sequence of the clinical activities of P in future. Comparative study shows better performance of the proposed algorithm over other state-of-the-art algorithms. © 2024 IEEE.

关键词： Electronic health record

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Optimized Deep Learning Classification Model for Intelligent Edge devices

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Journal of engineering Science and technology Review 2024年第3期17卷 88-94页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Electronics and Communication Engineering REVA University Karnataka Bangalore560064 India

Deep learning models enable state-of-the-art accuracy in computer vision applications. However, the deeper, computationally expensive, and densely connected architecture of deep neural networks (DNN) have limitations for deploying the model on resource-constraint embedded IoT devices. We propose an efficient neural network compression framework that performs filter pruning, fine-tuning and 8-bit quantization to reduce computational complexity, inference time, and memory footprint. Furthermore, reducing the bit widths of activation and weights helps design a compact deployment model on resource-limited IoT devices such as smartphones. The proposed system is evaluated extensively on the CIFAR-10 dataset for Resnet34 and VGG16 models. In addition, we examine the efficacy of a larger model. The result shows that pruning followed by quantization compresses the neural network and compared to the baseline model, achieved an accuracy of 78.01% for Resnet34 and 82.34% for Vgg16 after pruning and quantization which is © 2024 School of Science, DUTH. All rights reserved.

关键词： Object recognition

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Parameterized Clustering Cleaning Approach for High-Dimensional Datasets with Class Overlap and Imbalance

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SN computer Science 2023年第5期4卷 464页

作者： Goel, Navansh Singaravelu, Mohanapriya Gupta, Shivani Namana, Sriram Singh, Richa Kumar, Ranjeet School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India

Software Defect Prediction (SDP) uses machine learning algorithms to detect faulty and defective modules inside software projects. Like any machine learning model, the model’s performance depends on the training data. The ongoing research in the field explores various options for improving the quality, but class overlap and imbalance hinder the models’ effectiveness. Unwarranted instances grouped with relevant data act as noise, hindering the classification process. The removal of such instances can lead to improvement in the performance of standard machine learning classification models. This paper proposes a novel parameterized clustering approach, ‘CLARANSCA‘, for cleaning high dimensional datasets suffering from class overlap and imbalance. The parameterized clustering approach ‘CLARANS’ serves as the base for the proposed method. A multi-fold approach to calculating the evaluation metrics is used, ensuring consistency in results. The aggregate results show significant improvements, especially for Recall, AUC, and G-mean values. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC CLARANS CLARANSCA Class imbalance Class overlap G-mean Parameterized clustering Recall Software defect prediction

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A Review on Traditional and Deep Learning based Object Detection Methods 5

A Review on Traditional and Deep Learning based Object Detec...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Solunke, Babruvan R. Gengaje, Sachin R. Walchand Institute of Technology Solapur Computer Science and Engineering Solapur India Walchand Institute of Technology Solapur Electronics and Computer Engineering Solapur India

ISBN: (纸本)9781665475242

Fast and accurate object detection systems are in high demand due to the advent of autonomous vehicles, smart video surveillance, facial detection, and numerous people counting applications. These systems not only detect and classify every object in an image or video, but also locate each one by creating a bounding box around it. This paper analyses the traditional and recent deep learning-based object detection methods from different perspectives, incorporating features recognition on many scales, data expansion, training approach, and perspective detection, in order to make it easier to deeply understand object detection. Some commonly used standard datasets for object detection are discussed. It also addressed the challenges and possible research scope in the future from the perspective of evolving object detection datasets and the framework for object detection tasks. From the analysis, it is observed that the performance of the methods in use for object detection is moderate and requires improvement, especially in difficult environments such as large object scale variance, obstructed object view, and horrific mild prerequisites. Therefore, the possible research scope for inventions and implementation of more novel deep learning methods to enhance object detection and classification accuracy is discussed. © 2023 IEEE.

关键词： Object detection

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Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

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computer Systems Science & engineering 2023年第5期45卷 1545-1559页

作者： P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik Department of Information Technology SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all ***,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper *** present decade,the application of deep learning models in many fields of research has created greater *** increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil *** that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil ***,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)***,soil nutrient data has been given as second input to the DNNR *** utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been *** training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the *** results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification *** results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

关键词： Soil quality classification accuracy deep learning neural network soil features training and testing

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Energy Measurement by Smart Metering using Blockchain technology 10

Energy Measurement by Smart Metering using Blockchain Techno...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Aishwarya, R. Chandrakala, D. Jeyanthi Yegammai, S. Varrshini, V. Kanaga Suba Raja, S. Vishnu Prasad, V. Easwari Engineering College Electrical & Electronics Engineering Chennai India SRM Institute of Science and Technology Computer Science & Engineering Tiruchirappalli India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353778

The development of demand-side management with controlled loads has received a lot of attention as a result of the smart grid's ongoing growth and the energy market's volatility. The large number of household loads in the residential sector has generated a great deal of interest in managing the energy consumption of individual residences. This paper suggests simulating the monitoring of several SHEMS (smart home energy management system) characteristics. We have concentrated on the functionalities of the suggested design that gather data and analyse it, and we'll apply them to common home loads like electric water heaters and charging stations for electric cars. Overall, we discovered that the modelling of the loads results in considerable financial savings for consumers when a SHEMS is put into place. Here, we'll use blockchain technology to transfer data securely. Every piece of energy data has a timestamp and a hash value. Since blockchain technology operates through code and encrypts all data, it is becoming more and more difficult for hackers to have a field day. Additionally, since all records are linked to one another via time stamps and hash codes, any change would have a significant impact on the entire database. © 2024 IEEE.

关键词： Energy utilization

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Upshot of unified power flow controller on the minimization of the severity of overloading on electric power grid

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Journal of King Saud University - engineering Sciences 2024年第6期36卷 400-408页

作者： Adetona, S.O. Olaboyin, E.B. Salawu, R.I. Okafor, F.N. Department of Electrical & Electronics Engineering University of Lagos Lagos Nigeria Department of Electronic & Computer Engineering Bells University of Technology Ogun State Ota Nigeria Nigerian Electricity Regulatory Commission Abuja Nigeria

With the objective of discovering the vital line in an electrical power grid (EPG) where the unified power flow controller (UPFC) could be inserted to minimize the severity of overloading (SOL) during contingencies, an investigation into the stiffness of single line contingencies (SLC) on the EPG is presented in this study. An overloading index (OVI) is developed and used in this contribution to arrange the transmission branches of the EPG based on their cruciality and criticality throughout SLC. A harmony search algorithm (HSA), an optimization method, has been adopted to optimally set the parameters of the UPFC to achieve the best minimum SOL of the system at the optimal location. The IEEE 30-bus network was used as the test bed. Results based on the test bed show that placing a UPFC based on the ranking of a well-known contingency severity index (CSI) minimized the SOL to 2.40×1010 as against the 8.05×105 obtained when the placement is done on the ranking based on the proposed OVI. The minimization of SOL is achieved with a reactance and reactive power of 0.036pu and 0.096pu, respectively, of the UPFC. The result also reveals that the proposed OVI identifies with a higher level of precision the vital line in the test bed for placing the UPFC for the purpose of minimizing the SOL during contingencies. © 2021 The Authors

关键词： Flow control

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